The present embodiments relate to automated lung lobe segmentation. Knowledge of lobar boundaries is critical for radiologist reports and treatment planning. For example, if a patch of lung disease is found, knowing the lobar region helps with automated reporting and planning treatment or intervention. Knowing the completeness of a fissure separating lobar regions may also aid determining the future success of certain lobectomy procedures. Fissure regions may also be of assistance when performing a deformable registration across time points in volumes.
Fissures, which separate the lungs into lobar regions, appear as thin relatively hyperintense lines in computed tomography (CT) images. The fissure may vary in thickness and may be incomplete or missing in some patients. Accurate segmentation of the lungs into lobar regions is a difficult task for computers due to the wide variation of anatomies and imaging artifacts. Some common difficulties include: missing or incomplete fissures due to being too faint for the image resolution or truly missing from the particular patient, and image artifacts such as noise or patient motion. The typical absence of vessels near the fissures is commonly used to aid in automated lobe segmentation; however the existence of vessels crossing fissure boundaries is known to occur in some patients, presenting additional difficulties for automated methods.
Prior methods of automated lobar segmentation or fissure identification may have problems dealing with variance in fissure completeness and vessel crossings. A machine-learning approach based on the values of the Hessian matrix has been proposed for lobar segmentation, but there are difficulties in acquiring enough training data. Annotation of fissures on a CT volume is a difficult and tedious task. The experience and knowledge necessary to identify the fissures as well as their widely different appearance limits the number of users who can annotate the images. Even with machine learning, problems due to anatomic variation are not completely removed.